Characterization of p120-catenin, a novel RSK substrate in the Ras/MAPK signalling pathway

La voie de signalisation Ras/mitogen-activated protein kinase (Ras/MAPK) occupe un rôle central dans la régulation de différents processus biologiques tels que la croissance, la survie mais aussi la prolifération cellulaire. En réponse à des signaux extracellulaires, cette voie de signalisation mène à l’activation des protéines ERK1/2, impliquées dans l’activation de nombreux substrats cellulaires dont les protéines kinases RSK (p90 ribosomal S6 kinase). Ces protéines kinases sont, entre autres, impliquées dans l’invasion et la migration cellulaire mais les mécanismes responsables de ces phénomènes biologiques restent inconnus à ce jour. Dans mon mémoire, je développe tout d’abord les travaux précédemment réalisés dans notre laboratoire, et identifie la protéine p120-Catenin (p120ctn), un composant majeur des jonctions adhérentes (AJ), comme un nouveau substrat de la voie Ras/MAPK. En utilisant notamment un anticorps phospho-spécificique, nous avons pu démontrer que p120ctn est phosphorylée sur la sérine 320, un nouveau site de phosphorylation, d’une manière dépendante des kinases RSK. D’autre part, nous avons trouvé que la signalisation Ras/MAPK réduit l’interaction entre les protéines p120ctn et N-cadhérine. Ainsi, nos observations suggèrent que l’activation de la voie Ras/MAPK est impliquée dans la diminution de l’adhérence entre cellules par la déstabilisation des AJ. Compte tenu du rôle primordial de la voie de signalisation Ras/MAPK dans le cancer, ce mécanisme nouvellement décrit pourrait contribuer à l’avancement des connaissances sur le développement des cancers dépendents de cette voie de signalisation.The Ras/MAPK (mitogen-activated protein kinase) signalling pathway is vital in regulating cell growth, survival and proliferation in response to extracellular signals. Positioned downstream in the pathway, the p90 ribosomal S6 kinase (RSK) family regulates cell invasion by weakening cell-cell adhesion, but the mechanisms involved remain elusive. In this thesis, I expand upon previous work performed in our lab and identify p120ctn, a major component of adherens junctions (AJ), as a new substrate of the Ras/MAPK pathway. Using a phospho-specific antibody, we demonstrate that p120ctn is phosphorylated on a new phosphorylation site on S320 upon activation of MAPK signalling in a RSK-dependent manner. Furthermore, we show that Ras/MAPK signaling reduces p120ctn binding to N-cadherin, suggesting a new mechanism by which MAPK activity decreases cell-cell adhesion by destabilizing AJs. Finally, we designed and optimized two individual assays to be used in future experiments examining the effects of Ras/MAPK signalling on AJ function. Taken together, our data identifies RSK as a regulator of p120ctn phosphorylation, and also implicates Ras/MAPK signalling in regulating cell-cell adhesion by destabilizing AJ through p120ctn. Given the role of Ras/MAPK signalling in cancer, this new mechanism may play a role in the development and progression of Ras-driven cancers

Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey

A small fraction of giants possess photospheric lithium (Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giant sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A (Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE = 0.15 dex, and σ = 0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A (Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants

A novel collaborative control algorithm for maximum power point tracking of wind energy hydraulic conversion system

Abstract Wind has been admitted as one of the most promising renewable energy resources in multinational regionalization policies. However, the energy conversion and utilization are challenging due to the technique reliability and cost issues. Hydraulic wind turbine (HWT) may solve the above problems. HWT is taken as a research object, and the maximum power point tracking (MPPT) control strategy is proposed collaborating with active disturbance rejection control (ADRC) and linear quadratic regulator (LQR) control methods, to solve multiplicative nonlinearity problems in the plant models and the influence of external disturbance on control performance in the MPPT control process. A nonlinear simulation model is built to explain the main findings from the experiments and obtain a better understanding of the effect of time‐varying system parameters and random fluctuation in wind speed. The collaborative control algorithm is experimentally verified on a 24‐kW HWT semi‐physical test platform that results in a promising energy conversion rate, plus the hydraulic parameters can satisfy the demand, accordingly. Ultimately, the potential challenges of implementing this technique in a smart wind energy conversion system are discussed to give a further design guidance, either theoretically or practically

The complete mitochondrial genome of Holothuria spinifera (Théel, 1866)

In this research, on an Illumina platform, the full mitochondrial genome of Holothuria spinifera was listed in a sequence and also gathered by using the NovoPlasty v. 2.7.1. It was submitted to NCBI GenBank, and is available with accession number MN816440. The size of genome was 15,812 bp and contained 12 protein-coding genes, two rRNA genes, and 22 tRNA genes. The configuration of A + T in Holothuria spinifera mtDNA was 60.44%. Except five tRNAs and ND6, others are placed on the H-strand. By using the Neighbor-Joining method by software MEGA5.0, the phylogenetic relationship of 13 species of sea cucumber was analyzed. Holothuria spinifera was most closely associated to Parastichopus parvimensis

Proteomic Analysis Reveals a Role for RSK in p120-catenin Phosphorylation and Melanoma Cell-Cell Adhesion.

The RAS/mitogen-activated protein kinase (MAPK) signaling pathway regulates various biological functions, including cell survival, proliferation and migration. This pathway is frequently deregulated in cancer, including melanoma, which is the most aggressive form of skin cancer. RSK (p90 ribosomal S6 kinase) is a MAPK-activated protein kinase required for melanoma growth and proliferation, but relatively little is known about its function and the nature of its cellular partners. In this study, we used a proximity-based labeling approach to identify RSK proximity partners in cells. We identified many potential RSK-interacting proteins, including p120ctn (p120-catenin), which is an essential component of adherens junction (AJ). We found that RSK phosphorylates p120ctn on Ser320, which appears to be constitutively phosphorylated in melanoma cells. We also found that RSK inhibition increases melanoma cell-cell adhesion, suggesting that constitutive RAS/MAPK signaling negatively regulates AJ integrity. Together, our results indicate that RSK plays an important role in the regulation of melanoma cell-cell adhesion

Deep learning-guided selection of antibody therapies with enhanced resistance to current and prospective SARS-CoV-2 Omicron variants

Most COVID-19 antibody therapies rely on binding the SARS-CoV-2 receptor binding domain (RBD). However, heavily mutated variants such as Omicron and its sublineages, which are characterized by an ever increasing number of mutations in the RBD, have rendered prior antibody therapies ineffective, leaving no clinically approved antibody treatments for SARS-CoV-2. Therefore, the capacity of therapeutic antibody candidates to bind and neutralize current and prospective SARS-CoV-2 variants is a critical factor for drug development. Here, we present a deep learning-guided approach to identify antibodies with enhanced resistance to SARS-CoV-2 evolution. We apply deep mutational learning (DML), a machine learning-guided protein engineering method to interrogate a massive sequence space of combinatorial RBD mutations and predict their impact on angiotensin-converting enzyme 2 (ACE2) binding and antibody escape. A high mutational distance library was constructed based on the full-length RBD of Omicron BA.1, which was experimentally screened for binding to the ACE2 receptor or neutralizing antibodies, followed by deep sequencing. The resulting data was used to train ensemble deep learning models that could accurately predict binding or escape for a panel of therapeutic antibody candidates targeting diverse RBD epitopes. Furthermore, antibody breadth was assessed by predicting binding or escape to synthetic lineages that represent millions of sequences generated using in silico evolution, revealing combinations with complementary and enhanced resistance to viral evolution. This deep learning approach may enable the design of next-generation antibody therapies that remain effective against future SARS-CoV-2 variants

Predictive profiling of SARS-CoV-2 variants by deep mutational learning

The continual evolution of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the emergence of variants that show resistance to vaccines and neutralizing antibodies (1–4) threaten to prolong the coronavirus disease 2019 (COVID-19) pandemic (5). Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine learning-guided protein engineering technology, which is used to interrogate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as omicron (B.1.1.529), thus supporting decision making for public heath as well as guiding the development of therapeutic antibody treatments and vaccines for COVID-19

Depositional architecture and evolution of basin-floor fan systems since the Late Miocene in the Northwest Sub-Basin, South China Sea

The sediment budget of the Northwest Sub-basin, South China Sea since the Late Miocene (11.6 Ma, average thickness > 1000 m) accounts for more than two-thirds of the total infill since the initial ocean spreading of the sub-basin (32 Ma). The sediment sources and architectural pattern of these deposits, however, are poorly known. Using high-resolution 2D reflection seismic data with age constraint from IODP boreholes, we have documented two interdigitating basin-floor fan systems that developed since the Late Miocene. These were fed by two of the largest deep-water canyon systems worldwide, from the west (the Central Canyon/Xisha Trough) and the northeast (the Pearl River Canyon), as well as from smaller headless canyons and gullies across the surrounding slopes. Based on careful analysis of seismic facies, their geometry and occurrence, we identify the principal deepwater architectural elements, including the multi-scale channels, channel-levee complexes, lobes, sheets and drapes, mass-transport deposits, volcanic intrusions, turbidity-current sediment-wave fields, and a contourite drift/terrace. Tentative reconstructions show that the development of these Late Miocene-Quaternary basin-floor fan systems was dominated by changes of sediment supply. The Xisha fan reached its largest extent during the Late Miocene, while the Pearl River fan was most active during the Late Miocene to Quaternary. During the Late Miocene, both the conduits of the Central Canyon and the Pearl River Canyon were active with abundant sediment supply, generating the two incipient fan systems. Sediment supply from the west via the Central Canyon persisted throughout the Late Miocene, being coarser-grained than that of the Pearl River fan. With the demise of the Central Canyon during the Pliocene and consequent sharp decrease in sediment supply, the Xisha fan size reduced significantly. By contrast, supply of mud-rich sediments from the Pearl River and northern slope increased through the Pliocene and into the Quaternary, leading to the modern sedimentary pattern of interdigitating basin-floor fans. Insights into the evolution of sediment supply and fan development through time derived in this study contribute to a better understanding of how source to sink systems feed marginal oceanic basins such as the South China Sea

Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19

DeepSARS: simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2

Background The continued spread of SARS-CoV-2 and emergence of new variants with higher transmission rates and/or partial resistance to vaccines has further highlighted the need for large-scale testing and genomic surveillance. However, current diagnostic testing (e.g., PCR) and genomic surveillance methods (e.g., whole genome sequencing) are performed separately, thus limiting the detection and tracing of SARS-CoV-2 and emerging variants. Results Here, we developed DeepSARS, a high-throughput platform for simultaneous diagnostic detection and genomic surveillance of SARS-CoV-2 by the integration of molecular barcoding, targeted deep sequencing, and computational phylogenetics. DeepSARS enables highly sensitive viral detection, while also capturing genomic diversity and viral evolution. We show that DeepSARS can be rapidly adapted for identification of emerging variants, such as alpha, beta, gamma, and delta strains, and profile mutational changes at the population level. Conclusions DeepSARS sets the foundation for quantitative diagnostics that capture viral evolution and diversity